AI Strategy & Transformation in Greece

My approach starts with the business problem, not the AI tool. The first question is whether there is a task, decision or service where AI can make a clear difference and where the organization can actually use the result.

How I assess an AI use case

I look at the problem itself, who owns it, the information available and what would improve if the use case worked. I also look at what happens when the output is wrong, what requires human review, whether the people involved will use it and whether the idea is worth maintaining beyond a pilot.

Some good AI opportunities are obvious: repetitive analysis, fragmented information, slow reporting, difficult prioritization or work that depends on reading and comparing large volumes of material. Others are less suitable because the problem is poorly defined, the source information is weak or the risk of error is too high for the benefit.

Testing is not the same as transformation

A prototype can show that a model is capable of doing something. That is only the first step. Regular business use also needs ownership, a clear workflow, source and quality checks, training, integration and a way to measure whether the solution is helping.

I normally separate three questions: can it work, can it work reliably enough for this use case, and can the organization operate it responsibly over time? Many AI experiments answer the first question and never reach the other two.

Internal AI, client services and custom products

My current role covers three different contexts. Internal AI is mainly about improving the way teams work, including analysis, reporting, knowledge access and productivity. AI-enabled client services use AI to strengthen an existing advisory or intelligence service. Custom products make sense when a specific client or organizational need requires a purpose-built system.

These contexts need different levels of investment, integration, governance and support. Treating them as one portfolio usually creates the wrong expectations.

Working in the Greek market

In Greece, I often see very different levels of AI maturity inside the same organization. A small group may be testing advanced use cases while other teams are still deciding what is allowed, useful or safe. Greek organizations also work across Greek and English content, local and international media and European privacy and governance requirements.

That makes local context important. A generic English-language demo may look good and still need significant work before it fits the real workflow, language, source environment and risk profile of an organization operating in Greece.

What success looks like

The measure depends on the use case. It may be time saved, faster analysis, better consistency, stronger source quality, earlier risk detection, higher adoption or new commercial value. The important part is deciding what success means before scaling the work.